Parallel Higher-order Truss Decomposition

TL;DR

This paper introduces the first parallel framework for higher-order truss decomposition, enabling efficient analysis of hierarchical structures in large networks, with optimizations and experiments on real-world data.

Contribution

It presents the first parallel approach for higher-order truss decomposition, addressing previous non-parallel limitations and enhancing scalability.

Findings

The parallel framework significantly accelerates decomposition.

Optimizations improve processing efficiency.

Experiments confirm effectiveness on real-world networks.

Abstract

The k-truss model is one of the most important models in cohesive subgraph analysis. The k-truss decomposition problem is to compute the trussness of each edge in a given graph, and has been extensively studied. However, the conventional k-truss model is difficult to characterize the fine-grained hierarchical structures in networks due to the neglect of high order information. To overcome the limitation, the higher-order truss model is proposed in the literature. However, the previous solutions only consider non-parallel scenarios. To fill the gap, in this paper, we conduct the first research to study the problem of parallel higher-order truss decomposition. Specifically, a parallel framework is first proposed. Moreover, several optimizations are further developed to accelerate the processing. Finally, experiments over 6 real-world networks are conducted to verify the performance of proposed methods.